ISC CFDML workshop 2020 : CALL FOR PAPERS: FIRST INTERNATIONAL WORKSHOP ON THE APPLICATION OF MACHINE LEARNING TECHNIQUES TO COMPUTATIONAL FLUID DYNAMICS SIMULATIONS AND ANALYSIS (CFDML)
Call For Papers
The combination of computational fluid dynamics (CFD) with machine learning (ML) is a newly emerging research direction with the potential to enable solving so far unsolved problems in many application domains. Machine learning is already applied to a number of problems in CFD, such as identification and extraction of hidden features in large-scale flow computations, finding undetected correlations between dynamical features of the flow, and generating synthetic CFD datasets through high-fidelity simulations. These approaches are forming a paradigm shift to change the focus of CFD from time-consuming feature detection to in-depth examinations of such features, and enabling deeper insight into the physics involved in complex natural processes.
Apart from pure fluid dynamics, the community is embracing ML in a number of areas, such as constitutive modeling of heterogeneous materials, multiphase flow modeling, dynamics of the atmospheric, ocean, and climate system, and combustion/chemical reactions.
The workshop will stimulate this research by providing a venue to exchange new ideas and discuss challenges and opportunities as well as expose this newly emerging field to a broader research community. We aim to bring together researchers and industrial practitioners working on any aspects of applying ML to the CFD and related domains in order to provide a venue for discussion, knowledge transfer, and collaboration among the research community.
We are soliciting papers on all aspects of CFD where ML plays a significant role or enables the solution of complex problems in CFD and related fields. Topics of interest include, but are not limited to: physics-based modeling with the main focus on fluid physics, such as reduced modeling for dimensionality reduction and the Reynolds-averaged Navier-Stokes (RANS) turbulence modeling; shape and topology optimization in solids; prediction of aeroacoustics; uncertainty quantification and reliability analysis; reinforcement learning for the design of active/passive flow control, and any ML approach that enables or enhances any of the above techniques.
The workshop will consist of 20-minute talks and will conclude with a panel session, where experts working in the field will discuss the most pressing challenges and attempt to identify the most promising directions to continue developing in the near future. Accepted papers will be published in a Springer LNCS proceedings volume that will accompany the ISC proceedings volume.
Paper due: March 30, 2020
Acceptance notification: April 27, 2020
Camera ready: June 1, 2020
Workshop day: June 25, 2020
The members of the program committee will review papers submitted to the workshop. Submissions should follow the paper formatting instructions for LNCS and should be submitted via EasyChair. We expect papers to be 10 to 14 pages long, not counting the references. Submission of a paper should be regarded as a commitment that, should the paper be accepted, at least one of the authors will register and attend the conference to present the work. Accepted papers will be published in a Springer LNCS volume.
WORKSHOP CO-CHAIRS AND PROGRAM COMMITTEE
Volodymyr Kindratenko, National Center for Supercomputing Applications, USA
Andreas Lintermann, Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany
Charalambos Chrysostomou, The Cyprus Institute, Cyprus
Jiahuan Cui, Zhejiang University, China
Eloisa Bentivegna, IBM Research, UK
Ashley Scillitoe, The Alan Turing Institute, UK
Morris Riedel, University of Iceland, Iceland
Jenia Jitsev, Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany
Seid Koric, National Center for Supercomputing Applications, USA
Shirui Luo, National Center for Supercomputing Applications, USA
Madhu Vellakal, National Center for Supercomputing Applications, USA
Jeyan Thiyagalingam, Science and Technology Facilities Council, UK